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https://issues.apache.org/jira/browse/SPARK-3573?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14140206#comment-14140206
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Egor Pakhomov commented on SPARK-3573:
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Can you tell more about your motivation to use ShemeRDD as class for Dataset?
For me it seems that there are some problems with such decision:
1) Dataset has no connection with sql, rows or relational stuff. Make these two
very different topic coexist in one class is quite dangerouse.
2) The only thing I see, which ShemeRDD and dataset have in common is some
structure inside RDD. But this structure is very different - one is about rows
- another is about features and labeles. Not to mention collabarative
filtering algorithms which could contain very different structure.
I can't see what motivate us use ShemeRDD as abstraction for dataset.
> Dataset
> -------
>
> Key: SPARK-3573
> URL: https://issues.apache.org/jira/browse/SPARK-3573
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Priority: Critical
>
> This JIRA is for discussion of ML dataset, essentially a SchemaRDD with extra
> ML-specific metadata embedded in its schema.
> .Sample code
> Suppose we have training events stored on HDFS and user/ad features in Hive,
> we want to assemble features for training and then apply decision tree.
> The proposed pipeline with dataset looks like the following (need more
> refinements):
> {code}
> sqlContext.jsonFile("/path/to/training/events",
> 0.01).registerTempTable("event")
> val training = sqlContext.sql("""
> SELECT event.id AS eventId, event.userId AS userId, event.adId AS adId,
> event.action AS label,
> user.gender AS userGender, user.country AS userCountry,
> user.features AS userFeatures,
> ad.targetGender AS targetGender
> FROM event JOIN user ON event.userId = user.id JOIN ad ON event.adId =
> ad.id;""").cache()
> val indexer = new Indexer()
> val interactor = new Interactor()
> val fvAssembler = new FeatureVectorAssembler()
> val treeClassifer = new DecisionTreeClassifer()
> val paramMap = new ParamMap()
> .put(indexer.features, Map("userCountryIndex" -> "userCountry"))
> .put(indexer.sortByFrequency, true)
> .put(iteractor.features, Map("genderMatch" -> Array("userGender",
> "targetGender")))
> .put(fvAssembler.features, Map("features" -> Array("genderMatch",
> "userCountryIndex", "userFeatures")))
> .put(fvAssembler.dense, true)
> .put(treeClassifer.maxDepth, 4) // By default, classifier recognizes
> "features" and "label" columns.
> val pipeline = Pipeline.create(indexer, interactor, fvAssembler,
> treeClassifier)
> val model = pipeline.fit(raw, paramMap)
> sqlContext.jsonFile("/path/to/events", 0.01).registerTempTable("event")
> val test = sqlContext.sql("""
> SELECT event.id AS eventId, event.userId AS userId, event.adId AS adId,
> user.gender AS userGender, user.country AS userCountry,
> user.features AS userFeatures,
> ad.targetGender AS targetGender
> FROM event JOIN user ON event.userId = user.id JOIN ad ON event.adId =
> ad.id;""")
> val prediction = model.transform(test).select('eventId, 'prediction)
> {code}
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